Active safety functionalities in vehicles have grown into an important consideration in the auto industry. To improve the active safety functionalities, it is important to know accurate information about the road over which a vehicle is traveling as well as objects on and adjacent to the road (i.e., target objects). Due to the uncertainties of the sensor measurements and other factors such as for example an object's future behavior, it can be difficult if not impossible to acquire accurate information about the road over which the vehicle is traveling and the target objects. In most instances, it is only practical to reliably acquire the most probable information about the road over which the vehicle is traveling and the target objects. To induce the most probable information, it is well known to use the multiple sensors for acquiring information about the road over which the vehicle is traveling and the target objects.
Implementing fusion of information for a plurality of target objects is well known. However, the fusion of the road geometry model information from different sources such as, for example, vision systems, radar systems, electronic horizon (EH) system and the like has not yet been implemented in a comprehensive, efficient, or effective manner. Therefore, implementing the fusion of the road geometry model information (e.g., road geometry models) from different sources in a manner that is comprehensive, efficient, and effective would be beneficial, desirable and useful.